SOTAVerified

General Knowledge

This task aims to evaluate the ability of a model to answer general-knowledge questions.

Source: BIG-bench

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 176–200 of 399 papers

TitleStatusHype
Adapter-based Approaches to Knowledge-enhanced Language Models -- A Survey—0
GOT4Rec: Graph of Thoughts for Sequential Recommendation—0
GRL-Prompt: Towards Knowledge Graph based Prompt Optimization via Reinforcement Learning—0
Efficient Transfer Learning for Video-language Foundation ModelsCode0
MM-Eval: A Hierarchical Benchmark for Modern Mongolian Evaluation in LLMsCode0
Exploring Zero-Shot Anomaly Detection with CLIP in Medical Imaging: Are We There Yet?—0
SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing Agents—0
Extracting Unlearned Information from LLMs with Activation Steering—0
Evaluating Company-specific Biases in Financial Sentiment Analysis using Large Language Models—0
A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service RoboticsCode0
AdaptGCD: Multi-Expert Adapter Tuning for Generalized Category Discovery—0
Bridge-Coder: Unlocking LLMs' Potential to Overcome Language Gaps in Low-Resource Code—0
Fast constrained sampling in pre-trained diffusion models—0
Should We Really Edit Language Models? On the Evaluation of Edited Language ModelsCode0
Boosting LLM Translation Skills without General Ability Loss via Rationale Distillation—0
Large Language Models as a Tool for Mining Object Knowledge—0
Enhance Graph Alignment for Large Language Models—0
MANet: Fine-Tuning Segment Anything Model for Multimodal Remote Sensing Semantic Segmentation—0
Thinking LLMs: General Instruction Following with Thought Generation—0
Scalable Multi-Domain Adaptation of Language Models using Modular Experts—0
Distribution-aware Noisy-label Crack SegmentationCode0
Nudging: Inference-time Alignment of LLMs via Guided Decoding—0
Few Exemplar-Based General Medical Image Segmentation via Domain-Aware Selective Adaptation—0
Mars: Situated Inductive Reasoning in an Open-World Environment—0
Composite Learning Units: Generalized Learning Beyond Parameter Updates to Transform LLMs into Adaptive Reasoners—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Chinchilla-70B (few-shot, k=5)Accuracy94.3—Unverified
2Gopher-280B (few-shot, k=5)Accuracy93.9—Unverified
3Chinchilla-70B (few-shot, k=5)Accuracy 85.7—Unverified
4Gopher-280B (few-shot, k=5)Accuracy 84.8—Unverified
5Gopher-280B (few-shot, k=5)Accuracy84.2—Unverified
6Gopher-280B (few-shot, k=5)Accuracy 84.1—Unverified
7Gopher-280B (few-shot, k=5)Accuracy 83.9—Unverified
8Gopher-280B (few-shot, k=5)Accuracy83.3—Unverified
9Gopher-280B (few-shot, k=5)Accuracy 81.8—Unverified
10Gopher-280B (few-shot, k=5)Accuracy 81—Unverified